Redesign strategies of a comprehensive robust relief network for disaster management

We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues.     

Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises

The rapid diffusion of information is critical to combat the extreme levels of uncertainty and complexity that surround disaster relief operations. As a means of gathering and sharing information, humanitarian organizations are becoming increasingly reliant on social media platforms based on the Internet. In this paper, we present a field study that examines how effectively information diffuses through social media networks embedded in these platforms. Using a large dataset from Twitter during Hurricane Sandy, we first applied Information Diffusion Theory to characterize diffusion rates. Then, we empirically examined the impact of key elements on information propagation rates on social media. Our results revealed that internal diffusion through social media networks advances at a significantly higher speed than information in these networks coming from external sources. This finding is important because it suggests that social media networks are effective at passing information along during humanitarian crises that require urgent information diffusion. Our results also indicate that dissemination rates depend on the influence of those who originate the information. Moreover, they suggest that information posted earlier during a disaster exhibits a significantly higher speed of diffusion than information that is introduced later during more eventful stages in the disaster. This is because, over time, participation in the diffusion of information declines as more and more communications compete for attention among users.     

A multi-stage stochastic programming approach for pre-positioning of relief supplies considering returns

A dynamic pre-positioning problem is proposed to efficiently respond to victims’ need for relief supplies under uncertain and dynamic demand in humanitarian relief. The problem is formulated as a multi-stage stochastic programming model that considers pre-positioning with the dynamic procurement and return decisions about relief supplies over a time horizon. To validate the advantages of dynamic pre-positioning, three additional pre-positioning strategies are presented: pre-positioning with one-time procurement and without returns, pre-positioning with one-time procurement and returns, and pre-positioning with dynamic procurement and without returns. Using data from real-world disasters in the United States in the Emergency Events Database, we present a numerical analysis to study the applicability of the proposed models. We develop a sample average approximation approach to solving the proposed model in large-scale cases. Our main contribution is that we integrate dynamic procurement and return strategies into pre-positioning to decrease both costs and shortage risks in uncertain and dynamic contexts. The results illustrate that dynamic pre-positioning outperforms the other three strategies in cost savings. It also indicates that a higher return price is particularly helpful for decreasing unmet demand. The proposed models can help relief agencies evaluate and choose the solutions that will have the greatest overall effectiveness in the context of different relief practices.     

A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty

Disaster response operations revolve around uncertainties. While uncertainties arising due to randomness can be avoided for post-disaster location problem, those arising because of impreciseness may persist long after the disaster's occurrence. Despite the uncertainties and lack of sufficient information about the extent of the damage, disaster response facilities must be established quickly after the occurrence of the disaster. Moreover, the decisions of whether to open, where to locate, and when to open disaster response facilities are based on the amount and quality of information available during the decision-making period. To address these issues, we develop a multi-objective location-allocation model for relief supply and distribution that accounts for the imprecise and time-varying nature of different parameters and time-varying coverage, while also accommodating the subjective attributes necessary to enable establishment and operation of the temporary logistics hubs (TLHs). A credibility-based fuzzy chance-constrained programming model is employed to account for the impreciseness inherent in predicting parameter values during disaster response. The results show where, when, and how many TLHs to open and how to allocate relief supplies. Meanwhile, the sensitivity analysis provides a broader understanding of the impact of limiting the number of TLHs as well as the confidence level and the spread of the symmetric triangular fuzzy numbers on the attainment of the model objectives.     

Leveraging the social welfare chain to provide resilience during disaster

Due to the rampant increase in disasters globally, resilience in supply chain and logistics is receiving greater research attention in various parts of the world. This paper considers the role and contribution of social welfare supply chains in providing resilience. This is a causal phenomenon which has been studied through an exploratory research by building a framework from extant literature, a case study on an Indian non-governmental organization and tested through a social network analysis. We show how in the wake of a disaster, these social welfare supply chains become enablers. The paper provides an inkling into how to improve community resilience in times of disaster. Further, we show how these supply chains use social cohesion embedded in the structure to provide efficiency, diversity and adaptability, thereby bringing resilience into the supply system. Another contribution of this study is the use of visualisation and analysis of social media representation tools for social network analysis in operations.     

不同社交媒体平台上与企业相关的用户生成内容比较研究

本研究以小米和魅族两个手机品牌为例,采用关键词搜索和客观评判方法,对比了在新浪微博、人人网和优酷网上与这两个品牌相关的用户生成内容在数量、质量和情感方面的真实表现。通过对比研究发现：首先,不同社交媒体平台上与企业相关的用户生成内容在数量、质量和情感方面存在显著差异性;其次,企业在社交媒体上采用不同的营销策略对于与企业相关的用户生成内容数量、质量和情感有一定影响。这一研究结论弥补了理论界对此研究的不足,同时对于企业选择社交媒体开展营销活动有现实指导意义。     

Two managerial economics approaches to the R&D decision process

In this paper we present two different approaches that analyze the effects of rivals on the R&D decision process. The first approach assumes that the introduction time of the new product is uncertain to the manager. Given this assumption, the problem is analyzed in a framework similar to the one suggested by Kamien and Schwartz (1972). The second approch assumes technical certainty to study a set of models that extends and supports different views in the managerial economics literature regarding the properties of the R&D decision process from both private and social points of view.     

基于地震灾害的应急物流公共信息平台构建

近年来,我国地震灾害频发,主震、余震和次生自然灾害更加重了灾区的受灾状况.针对地震灾害的特殊性,首先对应急物流公共信息平台进行需求分析,然后基于此构建了一个由用户层、执行层、决策支持层、数据层和物理层的总体框架,该框架在执行层创新性地加入了的物流交易支持和突后管理模块,有效地整合社会力量进行救援和监督,综合考虑灾区救援争重建的两个环节.考虑到应急物流信息的标准化规范化的问题,在数据层创新性地加入了多元异构数据处理模块.     

Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits

Models for the distribution of relief supplies often assume immediate availability of relief items upon the occurrence of a natural disaster. However, such an assumption does not always apply in realistic settings. In some cases, at least it is necessary to assemble relief items into kits before distributing them among the affected population. This paper presents a rolling horizon methodology that considers dynamic parameters such as demand, capacities and demand priorities for the problem of distributing relief supplies after the occurrence of a natural disaster by including such assembling activities before the delivery.     

Assessing disaster risk by integrating natural and socio-economic dimensions: A decision-support tool

The paper provides a conceptual framework for a multi-dimensional assessment of risk associated to natural disasters. The different components of risk (hazard, exposure, vulnerability and resilience) are seen in a combined natural and socio-economic perspective and are integrated into a Disaster Risk Assessment Tool (DRAT). The tool can be used to support disaster management strategies, as well as risk mitigation and adaptation strategies at very disaggregated geographical or administrative scales. In this paper, we illustrate the features of the DRAT and we apply the tool to 7556 Italian municipalities to map their multidimensional risk. DRAT can be particularly useful to identify hotspots that are characterized by high hazard, exposure and vulnerability and by low resilience. In order to identify hotspots, we perform a cluster analysis of the Italian municipalities in terms of their risk ranking based on DRAT. We also suggest how the tool may be exploited within the processes of disaster risk policy.     

基于震后可用路网的应急救援物资配送路径方案研究

提出了决策震后应急救援物资配送路径方案三步骤理论,以时间效益最大目标为原则,建模分析了不同阶段救灾物资配送特点。首先运用Monte Carb模拟技术对网络连通性进行统计判断,形成对震后可用路网的重新构建。接着运用波动理论进行通行时间评估,得到全国多个物资配送中心多条到需求灾区的基于时间权值的可用路网图。最后对多个配送路线进行综合分析,建立最长救助时间最短的配送路线选择模型。     

A two-period newsvendor model for prepositioning with a post-disaster replenishment using Bayesian demand update

Humanitarian aid agencies usually resort to inventory prepositioning to mitigate the impact of disasters by sending emergency supplies to the affected area as quickly as possible. However, a lack of replenishment opportunity after a disaster can greatly hamper the effectiveness of the relief operation due to uncertainty in demand. In this paper, a prepositioning problem is formulated as a two-period newsvendor model where the response phase is divided into two periods. The model acknowledges the demand to be uncertain even after the disaster and utilises the Bayesian approach to revise the demand of the second period. Based on the revised demand, an order is placed at the beginning of the second period to be replenished instantaneously. A two-stage solution methodology is proposed to find the prepositioning quantity and post-disaster replenishment quantity, which minimise the total expected costs of relief operations. A numerical example is presented to demonstrate the solution methodology, and sensitivity analysis is performed to examine the effect of model parameters. The results highlight the indifferent characteristics shown by the replenishment quantity with the variation in model parameters.     

Risk management in humanitarian supply chain based on FMEA and grey relational analysis

The COVID-19 pandemic has disrupted the humanitarian supply chain management (HSCM) necessary for delivering emergency items during the disaster. The combined effects of climate change and the pandemic uncover the vulnerabilities of humanitarian supply chain operations and highlight the importance of risk management. This study aimed to identify priority risk factors and proposed mitigating risk strategies of a local government that is at the forefront of relief operations. It used Grey Relational Analysis (GRA) method to validate the Failure Mode and Effect Analysis (FMEA) approach in identifying priority issues relating to the supply chain risks. This paper reveals that the results of FMEA and GRA are almost similar.     

A dynamic model for disaster response considering prioritized demand points

This paper addresses the problem of distributing relief supplies after the occurrence of a disaster. We develop a dynamic model to serve demand, while prioritizing the response, according to the level of urgency of demand points. Our model is thought to be applied during a planning horizon and it considers dynamic demand, capacity constraints and priorities. To evaluate the applicability of our model, we use a real case study of a flood occurred in Colombia. We also test the computational solvability of our model and we propose and test different solution methodologies for solving larger instances of our problem.     

MANAGING DYSFUNCTIONAL EMOTIONS IN ORGANIZATIONS

Although much has been written about the different skills of managing people in work organizations there is a paucity of research and theorizing regarding a particular activity managers are often required to perform: that of dealing with the emotional behaviour of others. This paper aims to integrate research from clinical, social and occupational psychology with personal experience as a psychotherapist and management consultant to develop a framework of principles, strategies and tactics concerning how dysfunctional emotional behaviour of others can be managed effectively at work. the meaning of the term emotional behaviour is discussed and the issue of how emotional behaviour can be recognized is addressed. Five principles for managing dysfunctional emotional behaviour are outlined and strategies for the management of three common emotions (anger, anxiety, depression) are proposed, as well as consideration given to some specific tactics which illustrate how these principles and strategies can be implemented. Finally, guidelines concerning the management of different kinds of emotional reactions and the impact of organizational culture and emotional climate upon emotional behaviour are discussed along with the training implications of this framework.     

A disaster response model driven by spatial–temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts.     

Social Change: Measurement and Theory*

Societal change, which takes a variety of directions and forms and in no way can be assimilated or reduced to a single dimension, is often accompanied by a perception of insufficient understanding and lack of control. There is a frustrated need for mastering complexity and instability, separating the voluntary from the involuntary, the intended from the unintended, opportunities from risks, getting to the real causes and dominating the uncertain implications of social change. Social change catches us unprepared and confused. In this context statistics are generally considered a fundamental instrument of knowledge, but also part of the problem! In the public debate and in the specialized literature, the ability to measure social phenomena through current statistics and indicators is increasingly questioned. Data-it is claimed-are lacking, particularly longitudinal data; their quality (accuracy, relevance, timeliness, comparability, etc.) should be improved; indicators do not provide early warning signals, policy performance evaluation, and a precise indication of outcomes. Statistics cannot be used as a reliable and timely basis for decision making by individuals, organizations, governments, and for understanding these decisions. In some cases, statistics have been accused of giving a misleading and false picture of reality: do we measure the real extent of social exclusion and unemployment? Do we fully capture the quality of life and the degradation of the environment? Mismeasurement has been deemed by some commentators as being responsible for the wrong focus in inflation and stabilization policies, science and technology, unemployment and poverty. The productivity paradox, the informal economy, failure to measure welfare and the quality of urban life are instances where statistics do not seem to provide complete and satisfactory answers to the demand for information and knowledge. Our paper illustrates how, quite independently of measurement techniques and data production processes, the inadequacy of the conceptual framework may explain mismeasurement in relation to complex (multidimensional) and dynamic social phenomena. It is then to social theories, explanations and interpretations that statisticians need to turn, in order to come to grips with the new challenges in social measurement. We will develop this thesis looking at a few cases where measurement issues can be connected to both theoretical and empirical difficulties. The statistical gap which reveals itself in the mismeasurement or difficult measurement of social phenomena is closely interconnected with the social science gap. Only close collaboration between statisticians and social scientists can bring about continuous advancement in social science and quality improvement in social statistics.     

Multicriteria framework for selecting a process modelling language

The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.